Modelling of ROC curves in meta-analysis of diagnostic test accuracy studies and network meta-analysis

诊断测试准确性研究荟萃分析和网络荟萃分析中的 ROC 曲线建模

• 批准号: 214969570
• 负责人: Dr. Gerta Rücker
• 金额: --
• 依托单位: Institut für Medizinische Biometrie und Statistik (IMBI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/214969570?language=en
• 关键词: Modelling; ROC; curves; meta; analysis

This project refers to two active research areas of evidence synthesis in medicine, meta-analysis of diagnostic test accuracy studies and network meta-analysis. The final objective is to combine both areas.In standard approaches of meta-analysis of diagnostic accuracy studies, each study is assumed to contribute one pair of sensitivity and specificity. In the first period of the project, we investigated two more general approaches, which account for multiple thresholds of the underlying biomarker, `Modelling biomarker distributions' and `Averaging ROC curves'.The first approach is based on the idea of estimating the distribution functions of an underlying biomarker within the non-diseased and diseased individuals. Based on a distributional assumption, we estimate the distribution parameters in the two groups applying a linear mixed effects model to the appropriately transformed data. The model accounts for both the within-study dependence of sensitivity and specificity and between-study heterogeneity. We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index.In the second approach, originally introduced by Martinez-Camblor (2014), the summary ROC curve is determined as a weighted average of the ROC curves of the primary studies by averaging in `vertical' direction (i.e., averaging sensitivities, conditional on specificity). We extended this method by (1) exchanging the roles of sensitivity and specificity, i.e., averaging specificities conditional on sensitivity (horizontal averaging), and (2) averaging the differences of true positive rates and false positive rates (i.e., the Youden indices) conditional on their sum (diagonal averaging).Another area of research was network meta-analysis (NMA). In four publications, we investigated (1) the relation between NMA and electrical network theory, (2) an alternative method of adjusting for multi-arm studies by appropriately inflating the standard errors, (3) methods of automated visualisation of networks, and (4) frequentist treatment ranking, based on a network-meta-analysis.In the second project period we want to refine and extend all these approaches, with a special focus on knowledge translation. There is a more and more increasing spectrum of advanced methods on the one hand and their restricted accessibility for non-statistical users on the other hand. We want to bridge this gap and continue to follow this aim by writing a new R package for implementation of the `Modelling biomarker distributions' and `Averaging ROC curves' approaches and extending our existing R package netmeta for network meta-analysis, for example by meta-regression. The final objective is to combine meta-analysis of DTA studies and NMA to network meta-analysis of DTA studies.

该项目涉及医学证据合成、诊断测试准确性研究的荟萃分析和网络荟萃分析两个活跃的研究领域。最终的目标是联合收割机结合这两个领域。在诊断准确性研究的荟萃分析的标准方法中，每个研究都被假设为贡献一对敏感性和特异性。在该项目的第一阶段，我们研究了两种更通用的方法，它们解释了潜在生物标志物的多个阈值，“建模生物标志物分布”和“平均ROC曲线”。第一种方法是基于估计非患病和患病个体内潜在生物标志物的分布函数的想法。基于分布假设，我们对适当转换的数据应用线性混合效应模型估计两组中的分布参数。该模型解释了研究内敏感性和特异性的依赖性以及研究间异质性。我们获得了一个总结的受试者工作特征（SROC）曲线以及在每个特定阈值的合并灵敏度和特异性。在第二种方法中，最初由Martinez-Camblor（2014）引入，通过在“垂直”方向上取平均值（即，以特异性为条件的平均灵敏度）。我们通过（1）交换灵敏度和特异性的作用，即，以灵敏度为条件对特异性进行平均（水平平均），和（2）对真阳性率和假阳性率的差进行平均（即，另一个研究领域是网络元分析（NMA）。在四篇出版物中，我们研究了（1）NMA和电网络理论之间的关系，（2）通过适当增加标准误来调整多臂研究的替代方法，（3）网络自动可视化的方法，以及（4）基于网络荟萃分析的频率主义治疗排名。在第二个项目期间，我们希望改进和扩展所有这些方法，特别关注知识翻译。一方面，先进方法的种类越来越多，另一方面，非统计用户获得这些方法的机会有限。我们希望弥合这一差距，并继续遵循这一目标，编写一个新的R包，用于实现“建模生物标志物分布”和“平均ROC曲线”方法，并扩展我们现有的R包netmeta用于网络荟萃分析，例如通过荟萃回归。最终目标是将联合收割机DTA研究的荟萃分析和NMA结合到DTA研究的网络荟萃分析中。